In the present paper,we give a systematic study of the discrete correspondence the-ory and topological correspondence theory of modal meet-implication logic and moda1 meet-semilattice logic,in the semantics provided i...In the present paper,we give a systematic study of the discrete correspondence the-ory and topological correspondence theory of modal meet-implication logic and moda1 meet-semilattice logic,in the semantics provided in[21].The special features of the present paper include the following three points:the first one is that the semantic structure used is based on a semilattice rather than an ordinary partial order,the second one is that the propositional vari-ables are interpreted as filters rather than upsets,and the nominals,which are the“first-order counterparts of propositional variables,are interpreted as principal filters rather than principal upsets;the third one is that in topological correspondence theory,the collection of admissi-ble valuations is not closed under taking disjunction,which makes the proof of the topological Ackermann 1emma different from existing settings.展开更多
Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future...Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation(6G)wireless networks.In this paper,we propose a Chinese text semantic extraction model,namely T-Pointer,to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network.The proposed T-Pointer model consists of a semantic encoder and a semantic decoder.In the encoding stage,we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text.In the decoding stage,we first use the Transformer to extract multi-level global text features.Then,we introduce the pointer-generator network model to directly copy the keyword information from the source text.The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy(BLEU)and recalloriented understudy for gisting evaluation(ROUGE)by 14.69%and 14.87%on average in comparison with the state-of-the-art models,respectively.Also,we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral(USRP)platform.The result shows that the packet delay of semantic transmission can be reduced by 52.05%on average,compared to traditional information transmission.展开更多
Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier resear...Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.展开更多
Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the lo...This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the loss function ratio and integrating an attention mechanism(CBAM).First,the loss function weights were adjusted dynamically.The grid search method is used for deciding the best ratio of 7:3.It gives greater emphasis to the cross-entropy loss,which resulted in better segmentation performance.Second,CBAM was applied at different layers of the 2Dencoder.Heatmap analysis revealed that introducing it after the second block of 2D image encoding produced the most effective enhancement of important feature representation.The training epoch was chosen for optimizing the best value by experiments,which improved model convergence and overall accuracy.To evaluate the proposed approach,experiments were conducted based on the SemanticKITTI database.The results showed that the improved model achieved higher segmentation accuracy by 64.31%,improved 11.47% in mIoU compared with the conventional 2DPASS model(baseline:52.84%).It was more effective at detecting small and distant objects and clearly identifying boundaries between different classes.Issues such as noise and variations in data distribution affected its accuracy,indicating the need for further refinement.Overall,the proposed improvements to the 2DPASS model demonstrated the potential to advance semantic segmentation technology and contributed to a more reliable perception of complex,dynamic environments in autonomous vehicles.Accurate segmentation enhances the vehicle’s ability to distinguish different objects,and this improvement directly supports safer navigation,robust decision-making,and efficient path planning,making it highly applicable to real-world deployment of autonomous systems in urban and highway settings.展开更多
In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic q...In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.展开更多
This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee dr...This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest fre...While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.展开更多
Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation gener...Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural n...Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform en...Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform enhancement across the entire image,which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction.To overcome these limitations,we propose a Semantic-Guided Visual Mamba Network(SGVMNet)that unifies semantic reasoning,state-space modeling,and mixture-of-experts routing for adaptive illumination correction.SGVMNet comprises three key components:(1)a semantic modulation module(SMM)that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant(LLaVA)and Contrastive Language–Image Pretraining(CLIP)—and injects them hierarchically into the feature stream;(2)aMixture-of-Experts State-Space Feature EnhancementModule(MoE-SSMFEM)that dynamically selects informative channels and activates specialized state-space experts for efficient global–local illumination modeling;and(3)a Text-Guided Mixture Mamba Block(TGMB)that fuses semantic priors and visual features through bidirectional state propagation.Experimental results demonstrate that on the low-light(LOL)dataset,SGVMNet outperforms other state-of-the-art methods in both quantitative and qualitative evaluations,and it also maintains low computational complexity with fast inference speed.On LOLv2-Syn,SGVMNet achieves 26.512 dB PSNR and 0.935 SSIM,outperforming RetinexFormer by 0.61 dB.On LOLv1,SGVMNet attains 26.50 dB PSNR and 0.863 SSIM.Furthermore,experiments on multiple unpaired real-world datasets further validate the superiority of SGVMNet,showing that the model not only exhibits strong cross-scene generalization ability but also effectively preserves semantic consistency and visual naturalness.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a referen...Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.展开更多
Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),wh...Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.展开更多
Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the c...Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.展开更多
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.展开更多
基金supported by the Chinese Ministry of Education of Humanities and Social Science Project(23YJC72040003)the Key Project of Chinese Ministry of Education(22JJD720021)supported by the Natural Science Foundation of Shandong Province,China(project number:ZR2023QF021)。
文摘In the present paper,we give a systematic study of the discrete correspondence the-ory and topological correspondence theory of modal meet-implication logic and moda1 meet-semilattice logic,in the semantics provided in[21].The special features of the present paper include the following three points:the first one is that the semantic structure used is based on a semilattice rather than an ordinary partial order,the second one is that the propositional vari-ables are interpreted as filters rather than upsets,and the nominals,which are the“first-order counterparts of propositional variables,are interpreted as principal filters rather than principal upsets;the third one is that in topological correspondence theory,the collection of admissi-ble valuations is not closed under taking disjunction,which makes the proof of the topological Ackermann 1emma different from existing settings.
基金National Natural Science Foundation of China under Grants 62122069,62071431,62072490,62301490Science and Technology Development Fund of Macao,Macao,China under Grant 0158/2022/A+2 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515011287)MYRG2020-00107-IOTSCFDCT SKL-IOTSC(UM)-2021-2023。
文摘Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation(6G)wireless networks.In this paper,we propose a Chinese text semantic extraction model,namely T-Pointer,to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network.The proposed T-Pointer model consists of a semantic encoder and a semantic decoder.In the encoding stage,we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text.In the decoding stage,we first use the Transformer to extract multi-level global text features.Then,we introduce the pointer-generator network model to directly copy the keyword information from the source text.The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy(BLEU)and recalloriented understudy for gisting evaluation(ROUGE)by 14.69%and 14.87%on average in comparison with the state-of-the-art models,respectively.Also,we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral(USRP)platform.The result shows that the packet delay of semantic transmission can be reduced by 52.05%on average,compared to traditional information transmission.
基金financially supported by the China Atomic Energy Authority(CAEA)through the Geological Disposal Programthe National Natural Science Foundation of China(No.42307258)the China National Nuclear Corporation Fundamental Research Project(No.CNNC-JCYJ-202307)。
文摘Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
文摘This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model.Two novel improvements were proposed and implemented in this paper:dynamically adjusting the loss function ratio and integrating an attention mechanism(CBAM).First,the loss function weights were adjusted dynamically.The grid search method is used for deciding the best ratio of 7:3.It gives greater emphasis to the cross-entropy loss,which resulted in better segmentation performance.Second,CBAM was applied at different layers of the 2Dencoder.Heatmap analysis revealed that introducing it after the second block of 2D image encoding produced the most effective enhancement of important feature representation.The training epoch was chosen for optimizing the best value by experiments,which improved model convergence and overall accuracy.To evaluate the proposed approach,experiments were conducted based on the SemanticKITTI database.The results showed that the improved model achieved higher segmentation accuracy by 64.31%,improved 11.47% in mIoU compared with the conventional 2DPASS model(baseline:52.84%).It was more effective at detecting small and distant objects and clearly identifying boundaries between different classes.Issues such as noise and variations in data distribution affected its accuracy,indicating the need for further refinement.Overall,the proposed improvements to the 2DPASS model demonstrated the potential to advance semantic segmentation technology and contributed to a more reliable perception of complex,dynamic environments in autonomous vehicles.Accurate segmentation enhances the vehicle’s ability to distinguish different objects,and this improvement directly supports safer navigation,robust decision-making,and efficient path planning,making it highly applicable to real-world deployment of autonomous systems in urban and highway settings.
基金supported in part by the National Natural Science Foundation of China under Grant 62262073in part by the Yunnan Provincial Ten Thousand People Program for Young Top Talents under Grant YNWR-QNBJ-2019-237in part by the Yunnan Provincial Major Science and Technology Special Program under Grant 202402AD080002.
文摘In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.
基金National Natural Science Foundation of China under Grants No.62171047,U22B2001,62271065,62001051Beijing Natural Science Foundation under Grant L223027BUPT Excellent Ph.D Students Foundation under Grants CX2021114。
文摘This article studies the problem of image segmentation-based semantic communication in autonomous driving.In real traffic scenes,the detecting of objects(e.g.,vehicles and pedestrians)is more important to guarantee driving safety,which is always ignored in existing works.Therefore,we propose a vehicular image segmentation-oriented semantic communication system,termed VIS-SemCom,focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.First,we develop a semantic codec based on Swin Transformer architecture,which expands the perceptual field thus improving the segmentation accuracy.To highlight the important objects'accuracy,we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.Also,an importance-aware loss incorporating important levels is devised,and an online hard example mining(OHEM)strategy is proposed to handle small sample issues in the dataset.Finally,experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union(mIoU)performance in the SNR regions,a reduction of transmitted data volume by about 60%at 60%mIoU,and improve the segmentation accuracy of important objects,compared to baseline image communication.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
基金Supported by the National Natural Science Foundation of China(No.43277051)the Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education(No.B230203006).
文摘While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.
基金supported by the National Key Research and Development Program of China(2020AAA0109300)the Shanghai Collaborative Innovation Center of data intelligence technology(No.0232-A1-8900-24-13).
文摘Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
文摘Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
文摘Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform enhancement across the entire image,which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction.To overcome these limitations,we propose a Semantic-Guided Visual Mamba Network(SGVMNet)that unifies semantic reasoning,state-space modeling,and mixture-of-experts routing for adaptive illumination correction.SGVMNet comprises three key components:(1)a semantic modulation module(SMM)that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant(LLaVA)and Contrastive Language–Image Pretraining(CLIP)—and injects them hierarchically into the feature stream;(2)aMixture-of-Experts State-Space Feature EnhancementModule(MoE-SSMFEM)that dynamically selects informative channels and activates specialized state-space experts for efficient global–local illumination modeling;and(3)a Text-Guided Mixture Mamba Block(TGMB)that fuses semantic priors and visual features through bidirectional state propagation.Experimental results demonstrate that on the low-light(LOL)dataset,SGVMNet outperforms other state-of-the-art methods in both quantitative and qualitative evaluations,and it also maintains low computational complexity with fast inference speed.On LOLv2-Syn,SGVMNet achieves 26.512 dB PSNR and 0.935 SSIM,outperforming RetinexFormer by 0.61 dB.On LOLv1,SGVMNet attains 26.50 dB PSNR and 0.863 SSIM.Furthermore,experiments on multiple unpaired real-world datasets further validate the superiority of SGVMNet,showing that the model not only exhibits strong cross-scene generalization ability but also effectively preserves semantic consistency and visual naturalness.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
文摘Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.
基金supported by the Key Research and Development Project of Hubei Province(No.2024BAB070),China。
文摘Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.
文摘Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
